Algarni, Abdulmohsen and Li, Yuefeng and Tao, Xiaohui (2010) Mining specific and general features in both positive and negative relevance feedback. In: TREC 2010: 19th Text REtrieval Conference: Relevance Feedback Track, 16-19 Nov 2010, Gaithersburg, MD, USA.
User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovered knowledge are two critical problems. However, understanding what makes an individual document good or bad for feedback can lead to the solution of the previous problem. In TREC 2010, we participated in the Relevance Feedback Track and experimented two models for extracting pseudo-relevance feedback to improve the ranking of retrieved documents. The first one, the main run, was a pattern-based model, whereas the second one, the optional run, was a term-based model. The two models consisted of two stages: one using relevance feedback provided by TREC’10 to expand queries to extract pseudo-relevance feedback; one using pseudo-relevance feedback to find useful patterns and terms according to their relevance and irrelevance judgements to rank documents. In this paper, the detailed description of those models is presented.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||No evidence of copyright restrictions preventing deposit. Paper no. 33.|
|Uncontrolled Keywords:||survey; user feedback; change management; relevance; ranking|
|Depositing User:||epEditor USQ|
|Date Deposited:||18 May 2011 07:07|
|Last Modified:||03 Jul 2013 00:39|
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